SoroushVahidi/scidocs huggingface dataset:
Hedging your bets: Optimizing accuracy-specificity trade-offs in large scale visual recognition
(2012)

As visual recognition scales up to ever larger numbers of categories, maintaining high accuracy is increasingly difficult. In this work, we study the problem of optimizing accuracy-specificity trade-offs in large scale recognition, motivated by the observation that object categories form a semantic hierarchy consisting of many levels of abstraction. A classifier can select the appropriate level, trading off specificity for accuracy in case of uncertainty. By optimizing this trade-off, we obtain classifiers that try to be as specific as possible while guaranteeing an arbitrarily high accuracy. We formulate the problem as maximizing information gain while ensuring a fixed, arbitrarily small error rate with a semantic hierarchy. We propose the Dual Accuracy Reward Trade-off Search (DARTS) algorithm and prove that, under practical conditions, it converges to an optimal solution. Experiments demonstrate the effectiveness of our algorithm on datasets ranging from 65 to over 10,000 categories.
Corpus-Guided Sentence Generation of Natural Images
(2011)

We propose a sentence generation strategy that describes images by predicting the most likely nouns, verbs, scenes and prepositions that make up the core sentence structure. The input are initial noisy estimates of the objects and scenes detected in the image using state of the art trained detectors. As predicting actions from still images directly is unreliable, we use a language model trained from the English Gigaword corpus to obtain their estimates; together with probabilities of co-located nouns, scenes and prepositions. We use these estimates as parameters on a HMM that models the sentence generation process, with hidden nodes as sentence components and image detections as the emissions. Experimental results show that our strategy of combining vision and language produces readable and descriptive sentences compared to naive strategies that use vision alone.
Understanding user situational relevance in ranking web search results
(2017)

Relevance plays a key role in determining how ranking features are weighed in rank algorithms. In particular, the Search Engine Result Page (SERP) presents documents from the algorithmic relevance perspective. User relevance, however, is situational and describes the searcher's perception of the contextual document which is interpreted at rank time. Therefore, without it the monotonic decreasing click trend portrayed by rank algorithms will not hold for real time searchers. Instead, users select low ranked documents over high ones or reject the ranking, leading to frustration and several re-query tasks. To understand users situational relevance (USR), we formulate a decreasing probability function to measure each user's personalized probabilistic relevance (PPR). Next, we find an aggregated PPR representing the USR across several query categories. Then, we apply maximum probabilities swapping to modify the rank function and re-rank documents. Finally, we use Normalized Discounted Cumulative Gain (NDCG) and click weight metrics for evaluation. Our results show that a clear interpretation and incorporation of user situational relevance improves click satisfaction by 12.86%, and optimizes click through rate up to 42%.
3D reconstruction based on SIFT and Harris feature points
(2009)

This paper presents a new 3D reconstruction method using feature points extracted by the SIFT and Harris corner detector. Since the SIFT feature points can be detected stably and relatively accurately, the proposed algorithm first uses the SIFT matching points to calculate the fundamental matrix. On the other hand many of the feature points detected by the SIFT are not what we need for reconstruction, so by combining the SIFT feature points with the Harris corners it is possible to obtain more vivid and detailed 3D information. Experiments have been conducted to validate the proposed method.
GaN-based high frequency totem-pole bridgeless PFC design with digital implementation
(2015)

Totem-pole bridgeless PFC is a very promising topology for GaN devices because of very low reverse recovery. However, inherent challenges exist for this topology at zerocrossing point of AC voltage where inductor, current spike is observed which may cause higher harmonics and EMI issue. This paper looks deeply into this phenomena and found two main causes: the transition delay of low frequency leg and the minimum pulse width limit of high frequency leg. A DSP-based digital modulator is designed to address those two issues by applying lead time to the low frequency leg and digital dithering to increase equivalent resolution at the high frequency leg. Some other rules are also summarized to enable robust modulator design, which is the main challenge in this topology. A 500 kHz GaN totem-pole bridgeless PFC converter operating in continuous conduction mode verified the effectiveness of those methods to eliminate the current spike at zero-crossing. All those findings will help the implementation of wide band gap devices in very high frequency PFC circuit.
Vishleshan: Performance Comparison and Programming Process Mining Algorithms in Graph-Oriented and Relational Database Query Languages
(2015)

Process-Aware Information System (PAIS) are IT systems that manages, supports business processes and generate large event logs from execution of business processes. Process Mining consists of analyzing event logs generated by PAISs and discover business process models and check for conformance between the discovered and actual models. The large volume of event logs generated are stored in databases. Relational databases perform well for certain class of applications. However, there are certain class of applications for which relational databases are not able to scale. Several NoSQL databases have emerged to encounter the challenges of scalability in traditional databases. Discovering social network from event logs is one of the most challenging and important Process Mining task. Similar-Task algorithm is one of the most widely used Organizational Mining techniques. Our objective is to investigate which of the databases (Relational or Graph) perform better for Organizational Mining under Process Mining. We implement Similar-Task algorithm on relational and NoSQL (graph oriented) databases using only query language constructs. We conduct empirical analysis on a large real world data set to compare the performance of row-oriented database and NoSQL graph-oriented database.
Knowledge management infrastructure and processes on effectiveness of nursing care
(2018)

The purpose of this study is to investigate the influence of knowledge management infrastructure and process on nursing care effectiveness in selected teaching hospitals in Southwest Nigeria. The organization of nursing knowledge resources is critical to health care organizations for providing safe and high quality care for patients. Therefore, it is necessary to study the role of knowledge management in clinical nursing practice and the outcomes on the effectiveness of nursing care. Despite the critical role of nursing care in defining high-performing health care delivery, knowledge management in this area is still at an early stage of development. Based on organizational capability theory and employing concurrent mixed-method research design, this study seeks to identify the components of knowledge infrastructure and processes that influence the efficiency and effectiveness of nursing care in clinical floor nursing of selected teaching hospitals in Nigeria, a developing country in Africa. The output from the research is expected to contribute to the domain of literature/knowledge; provide awareness of the knowledge management practices in nursing care in Nigeria; assist nursing administrators, health policy makers and hospital management to exploit and make effective use of knowledge-based resources to enhance nursing care which can improve the productivity of health care organizations.
Bidirectional bridgeless PFC with reduced input current distortion and switching loss using gate skipping technique
(2016)

Bidirectional bridgeless PFC is an advantageous topology for high power application among many bridgeless PFCs due to small EMI filter size and no reverse recovery problem. However due to the large junction capacitance of secondary leg's diodes, the dead angle is shown in the zero-crossing area. Thus, the input current distortion is intensified, and the unnecessary switching loss decreases the efficiency. In this paper, a simple gate skipping technique is implemented to the zero-crossing area of the bidirectional bridgeless PFC. As a result, the unnecessary switching loss of boost switches and the input current distortion are reduced. The improvement of the proposed method is verified by experimental results with the high line input and 800W(400V/2A) output prototype.
Patient Monitoring System Using GSM Technology
(2013)

In this fast pace of life, it is difficult for people to be constantly available for their near ones who might need them while they are suffering from a disease or physical disorder. So also constant monitoring of the patient’s body parameters such as temperature, pulse rate, sugar level etc. becomes difficult. Hence to remove human error and to lessen the burden of monitoring patient’s health from doctor’s head, this paper presents the methodology for monitoring patients remotely using GSM network and Very Large Scale Integration (VLSI) technology. Patient monitoring systems measure physiological characteristics either continuously or at regular intervals of time.
AI Challenges in Human-Robot Cognitive Teaming
(2017)

Among the many anticipated roles for robots in the future is that of being a human teammate. Aside from all the technological hurdles that have to be overcome with respect to hardware and control to make robots fit to work with humans, the added complication here is that humans have many conscious and subconscious expectations of their teammates – indeed, we argue that teaming is mostly a cognitive rather than physical coordination activity. This introduces new challenges for the AI and robotics community and requires fundamental changes to the traditional approach to the design of autonomy. With this in mind, we propose an update to the classical view of the intelligent agent architecture, highlighting the requirements for mental modeling of the human in the deliberative process of the autonomous agent. In this article, we outline briefly the recent efforts of ours, and others in the community, towards developing cognitive teammates along these guidelines.
Bayesian policy reuse
(2016)

A long-lived autonomous agent should be able to respond online to novel instances of tasks from a familiar domain. Acting online requires ‘fast’ responses, in terms of rapid convergence, especially when the task instance has a short duration such as in applications involving interactions with humans. These requirements can be problematic for many established methods for learning to act. In domains where the agent knows that the task instance is drawn from a family of related tasks, albeit without access to the label of any given instance, it can choose to act through a process of policy reuse from a library in contrast to policy learning. In policy reuse, the agent has prior experience from the class of tasks in the form of a library of policies that were learnt from sample task instances during an offline training phase. We formalise the problem of policy reuse and present an algorithm for efficiently responding to a novel task instance by reusing a policy from this library of existing policies, where the choice is based on observed ‘signals’ which correlate to policy performance. We achieve this by posing the problem as a Bayesian choice problem with a corresponding notion of an optimal response, but the computation of that response is in many cases intractable. Therefore, to reduce the computation cost of the posterior, we follow a Bayesian optimisation approach and define a set of policy selection functions, which balance exploration in the policy library against exploitation of previously tried policies, together with a model of expected performance of the policy library on their corresponding task instances. We validate our method in several simulated domains of interactive, short-duration episodic tasks, showing rapid convergence in unknown task variations.
BotGuard: Lightweight real-time botnet detection in software defined networks
(2017)

The distributed detection of botnets may induce heavy computation and communication costs to network devices. Each device in related scheme only has a regional view of Internet, so it is hard to detect botnet comprehensively. In this paper, we propose a lightweight real-time botnet detection framework called Bot-Guard, which uses the global landscape and flexible configurability of software defined network (SDN) to identify botnets promptly. SDN, as a new network framework, can make centralized control in botnet detection, but there are still some challenges in such detections. We give a convex lens imaging graph (CLI-graph) to depict the topology characteristics of botnet, which allows SDN controller to locate attacks separately and mitigate the burden of network devices. The theoretical and experimental results prove that our scheme is capable of timely botnet detecting in SDNs with the accuracy higher than 90% and the delay less than 56 ms.
A Traceback Attack on Freenet
(2017)

Freenet is a popular peer to peer anonymous content-sharing network, with the objective to provide the anonymity of both content publishers and retrievers. Despite more than a decade of active development and deployment and the adoption of well-established cryptographic algorithms in Freenet, it remains unanswered how well the anonymity objective of the initial Freenet design has been met. In this paper we develop a traceback attack on Freenet, and show that the originating machine of a content request message in Freenet can be identified; that is, the anonymity of a content retriever can be broken, even if a single request message has been issued by the retriever. We present the design of the traceback attack, and perform both experimental and simulation studies to confirm the feasibility and effectiveness of the attack. For example, with randomly chosen content requesters (and random contents stored in the Freenet testbed), Emulab-based experiments show that, for <inline-formula><tex-math notation="LaTeX">$24$</tex-math> <alternatives><inline-graphic xlink:href="duan-ieq1-2453983.gif"/></alternatives></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$43$</tex-math><alternatives><inline-graphic xlink:href="duan-ieq2-2453983.gif"/> </alternatives></inline-formula> percent of the content request messages, we can identify their originating machines. We also briefly discuss potential solutions to address the developed traceback attack. Despite being developed specifically on Freenet, the basic principles of the traceback attack and solutions have important security implications for similar anonymous content-sharing systems.
Data summaries for on-demand queries over linked data
(2010)

Typical approaches for querying structured Web Data collect (crawl) and pre-process (index) large amounts of data in a central data repository before allowing for query answering. However, this time-consuming pre-processing phase however leverages the benefits of Linked Data -- where structured data is accessible live and up-to-date at distributed Web resources that may change constantly -- only to a limited degree, as query results can never be current. An ideal query answering system for Linked Data should return current answers in a reasonable amount of time, even on corpora as large as the Web. Query processors evaluating queries directly on the live sources require knowledge of the contents of data sources. In this paper, we develop and evaluate an approximate index structure summarising graph-structured content of sources adhering to Linked Data principles, provide an algorithm for answering conjunctive queries over Linked Data on theWeb exploiting the source summary, and evaluate the system using synthetically generated queries. The experimental results show that our lightweight index structure enables complete and up-to-date query results over Linked Data, while keeping the overhead for querying low and providing a satisfying source ranking at no additional cost.
Pure Pursuit Revisited: Field Testing of Autonomous Vehicles in Urban Areas
(2016)

In this paper, we aim to explore path following. We implement a path following component by referring to the existing Pure Pursuit algorithm. Using the simulation and field operational test, we identified the problem in the path following component. The main problems identified were with respect to vehicles meandering off the path, turning a corner, and the instability of steering control. Therefore, we apply some modifications to the Pure Pursuit[1] algorithm. We have also conducted the simulation and field operational tests again to evaluate these modifications.
An ontological analysis of the economic primitives of the extended-REA enterprise information architecture
(2002)

The Resource-Event-Agent (REA) model for enterprise economic phenomena was first published in The Accounting Review in 1982. Since that time its concepts and use have been extended far beyond its original accountability infrastructure to a framework for enterprise information architectures. The granularity of the model has been extended both up (to enterprise value chains) and down (to workflow or task specification) in the aggregation plane, and additional conceptual type-images and commitment-images have been proposed as well. The REA model actually fits the notion of domain ontology well, a notion that is becoming increasingly important in the era of e-commerce and virtual companies. However, its present and future components have never been analyzed formally from an ontological perspective. This paper intends to do just that, relying primarily on the conceptual terminology of John Sowa. The economic primitives of the original REA model (economic resources, economic events, economic agents, duality relationships, stock-flow relationships, control relationships and responsibility relationships) plus some newer components (commitments, types, custody, reciprocal, etc.) will be analyzed individually and collectively as a specific domain ontology. Such a review can be used to guide further conceptual development of REA extensions.
Point-of-Interest Demand Modeling with Human Mobility Patterns
(2017)

Point-of-Interest (POI) demand modeling in urban regions is critical for many applications such as business site selection and real estate investment. While some efforts have been made for the demand analysis of some specific POI categories, such as restaurants, it lacks systematic means to support POI demand modeling. To this end, in this paper, we develop a systematic POI demand modeling framework, named Region POI Demand Identification (RPDI), to model POI demands by exploiting the daily needs of people identified from their large-scale mobility data. Specifically, we first partition the urban space into spatially differentiated neighborhood regions formed by many small local communities. Then, the daily activity patterns of people traveling in the city will be extracted from human mobility data. Since the trip activities, even aggregated, are sparse and insufficient to directly identify the POI demands, especially for underdeveloped regions, we develop a latent factor model that integrates human mobility data, POI profiles, and demographic data to robustly model the POI demand of urban regions in a holistic way. In this model, POI preferences and supplies are used together with demographic features to estimate the POI demands simultaneously for all the urban regions interconnected in the city. Moreover, we also design efficient algorithms to optimize the latent model for large-scale data. Finally, experimental results on real-world data in New York City (NYC) show that our method is effective for identifying POI demands for different regions.
Credit Card Fraud Detection: A case study
(2015)

In this research, a technique for `Credit Card Fraud Detection' is developed. As fraudsters are increasing day by day. And fallacious transactions are done by the credit card and there are various types of fraud. So to solve this problem combination of technique is used like Genetic Algorithm, Behavior Based Technique and Hidden Markov Model. By this transaction is tested individually and whatever suits the best is further proceeded. And the foremost goal is to detect fraud by filtering the above techniques to get better result.
A Review on the ESD Robustness of Drain-Extended MOS Devices
(2012)

This paper reviews electrostatic discharge (ESD) investigations on laterally diffused MOS (LDMOS) and drain-extended MOS (DeMOS) devices. The limits of the safe operating area of LDMOS/DeMOS devices and device physics under ESD stress are discussed under various biasing conditions and layout schemes. Specifically, the root cause of early filament formation is highlighted. Differences in filamentary nature among various LDMOS/DeMOS devices are shown. Based on the physical understanding, device optimization guidelines are given. Finally, an outlook on technology scaling is presented.
A Survey on Cryptography and Steganography Methods for Information Security
(2010)

This paper deals with the tidings of cryptography in history, how it has played a vital role in World War -1, World War-2. It also deals with the Substitution, Transposition Cryptographic techniques and Steganography principles which can be used to maintain the confidentiality of computerized and none computerized information files. A number of well known techniques have been adapted for computer usage including the Ceaser cipher, Mono alphabetic cipher, Homophonic substitution, Bale cipher, Play fair cipher, Poly alphabetic cipher, Vigenere Cipher, Onetime pad cipher, Vernam ciphers, Play Color Cipher and usage of rotor machine in Substitutions, Rail fence technique, more complex permutations for more secure transposition and some Steganography principle were briefly discussed with merits and demerits. Finally it gives the broad knowledge on almost all the cryptographic and Steganography principles where a reader or scholar have lot of scope for updating or invention of more secure algorithms to fulfill the global needs in information security.